Probability Theory with Naive Bayes Application

Author: Daniel Hassler

Naive Bayes Background

Data

Naive Bayes Classification

Hyperparameter Tuning

Results

Improvements

Though we achieve decent accuracy with the MNIST dataset using MultinomialNB classifier, this model has one big flaw for image classification; it only looks at the discretized values for specific points in the image. What would happen if I shifted the “0” or “9” to a different section of the image (not centered)? it would not be able to classify this case effectively.

A way to fix the above limitation is using convolutional neural networks (CNNs), which is a deep learning classifier used in a lot of computer vision and even NLP related applications. Its main feature is using the idea of a “sliding window” to find more meaningful representations, which means the location of the object we’re classifying is less important.